Structural Optimization Design of Rotary Drilling Rig Drill Pipes Based on an Improved Enhanced Knowledge Gain Sharing Algorithm

To address the technical challenge of synergistic optimization between lightweight design and structural performance for mechanical locking drill pipes of rotary drilling rigs, this study takes such drill pipes as the research object. Seven typical operating conditions are classified to construct a multidimensional verification model encompassing static strength, stiffness, stability, and fatigue strength, while a lightweight optimization model with multi-performance constraints is established to minimize the cross-sectional area. Aiming at the limitations of the Enhanced Gaining–Sharing Knowledge (eGSK) algorithm in initial population distribution, integer constraint adaptation, and local exploration, an Improved Enhanced Gaining–Sharing Knowledge algorithm (ieGSK) is proposed, integrating hybrid initialization, integer solution processing, and elite local search mechanisms. Comparative tests with Enzyme-Activated Optimization (EAO), State-Based Optimization (SBO), and eGSK algorithms demonstrate that ieGSK converges to engineering-practical integer solutions in 258 iterations (computational efficiency, 4.475 s). Compared with EAO, SBO, and eGSK algorithms, the computational efficiency is improved by 61.6%, 43.1%, and 9.6%, respectively, while achieving a 9.8% weight reduction and maintaining optimal stability and robustness. This verifies the superiority of ieGSK in drill pipe structural optimization, offering technical support for the lightweight design of core components in rotary drilling rigs.